Organization of the practical

You will see different icons through the document, the meaning of which is:

 : additional or useful information
 : a worked example
 : a practical exercise
 : a space to answer the exercise
 : a hint to solve an exercise
 : a more challenging exercise

ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson’s Grammar of Graphics — a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. We will further learn about ggplot2 in our next theoretical session.

1a | Basic scatter plot

For the first problem we want to represent the relationship between the variables wt (weight) and mpg (miles/gallon) from the mtcars data frame.

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). You can type ?mtcars in the R console to read a description of the data.

To represent any graph in ggplot2 we need two basic functions that are combined with a + sign.

# Run install.packages("ggplot2") if ggplot2 is not yet installed
library(ggplot2)
ggplot(data = mtcars, mapping = aes(x = wt, y = mpg)) + 
  geom_point()

The variables that we want to represent are wrapped within an aes() function, that specifies the mapping between the variables and the aesthetic attributes (in this case we map them to spatial positions, x and y). We call the variables directly by their names, because we also pass the entire data frame to the call with the data argument, so ggplot knows were to get them from. Finally, we need to add the geometric object we want to represent. In this case, points.

1b | Represent extra variables

Another variable in the data indicates the number of cylinders of the car engines (cyl). There are cars with 4, 6 or 8 cylinders.

table(mtcars$cyl)

## 
##  4  6  8 
## 11  7 14

Let’s say we want to represent the different types of cylinders in different colours. In this case we want to use cyl as a categorical variable, distinguishing groups rather than indicating a value in a continuous scale. For that, we need to change its class before giving it to ggplot using the factor() function.

# First we check the class of cyl
class(mtcars$cyl)

## [1] "numeric"

# Because it is numeric, let's make cyl a factor so that we represent it as a categorical variable.
# We create a new variable in the dataframe, cyl_f, that is cyl converted to factor
mtcars$cyl_f <- factor(mtcars$cyl)

ggplot(data = mtcars, mapping = aes(x = wt, y = mpg, colour = cyl_f)) + 
  geom_point()

Note that ggplot adds a legend by default for all the variables that have been mapped to some aesthetic attribute. This way we can read all the variables without extra effort.

Exercise

Try mapping cyl_f to another aesthetic attribute instead of colour, such as shape or size.

ggplot(data = mtcars, mapping = aes(x = wt, y = mpg, shape = cyl_f)) + 
  geom_point()

What happens if you map a continuous variable such as qsec, instead of cyl, to colour? And to shape?

mtcars$cyl_f <- factor(mtcars$qsec)
ggplot(data = mtcars, mapping = aes(x = wt, y = mpg, color = cyl_f)) + 
  geom_point()+ggtitle("Color-based")

ggplot(data = mtcars, mapping = aes(x = wt, y = mpg, shape = cyl_f)) + 
  geom_point()+ggtitle("Shape-based")

A diferent category is generated for each data entry, as each data point is a different number

2a | Basic bar plot

We now want to summarise our data in a simple bar plot representing the number of cars in each cylinder category. However, the number of cars with 4 cylinders is not a piece of information present in the data set, for example. To know the number it is necessary to count the rows where cyl = 4.

ggplot2 is capable to do simple summary operations with the input variables, refered as statistical tranformations. One of them is to count the occurrences of each value in a variable. And geom_bar function happen to use the count statistical transformation by default on the variable mapped to the x axis.

ggplot(data = mtcars, mapping = aes(x = cyl)) +
  geom_bar()

# # The same as
#ggplot(data = mtcars, mapping = aes(x = cyl)) +
#   geom_bar(stat = "count")

If we had a precomputed data frame with cyl and number_of_cars instead, we could pass number_of_cars variable to geom_col function, that by default takes the variables mapped to x and y without transformation.

# Let's create the data frame
counts_by_cyl_data_frame <- as.data.frame(table(mtcars$cyl))
names(counts_by_cyl_data_frame) <- c("cyl", "number_of_cars")

ggplot(data = counts_by_cyl_data_frame, mapping = aes(x = cyl, y = number_of_cars)) +
  geom_col()

Alternatively, we could remove the default statistical transformation of geom_bar with stat = "indentity" and use the precomputed data frame.

# # The same as
# ggplot(data = counts_by_cyl_data_frame, mapping = aes(x = cyl, y = number_of_cars)) +
#   geom_bar(stat = "identity")

2b | Groups and position

We have seen in the scatter plot example how to represent groups encoded in extra variables as colours. Say we now want to show transmission type (am) in the bar plot, in addition to the number of cylinders. We can map am to the filling colour of the bars, fill. (colour would change the edges of the rectangles.)

# First we make am factor, and we can change the 0/1 notation for a more informative notation: automatic/manual
mtcars$am_f <- factor(mtcars$am, levels = c(0, 1), labels = c("automatic","manual"))

ggplot(data = mtcars, mapping = aes(x = cyl, fill = am_f)) +
  geom_bar()

Each geometric object in ggplot2 also has a position argument that controls how groups are arranged. In geom_bar the default position is to stack any groups. We can change it for a side-by-side position with position = "dodge".

ggplot(data = mtcars, mapping = aes(x = cyl, fill = am_f)) +
  geom_bar(position = "dodge")

Exercise

Which is the position argument in the ggplot2 bar plot that standardizes the bars to the same height? Update the plot above with the new position adjustment.

ggplot(data = mtcars, mapping = aes(x = cyl, fill = am_f)) +
  geom_bar(position = "fill")

Which position adjustment would you choose if you still wanted to compare the total amount of cars with each cylinder category? And if you were interested in knowing the relative abundance of each type of transmission?

To compare the total amount of cars per each cylinder category I would use the graph that has no specified position, only geom_bar().
To know the relative abudance of each type of transmission in relation to cylinders, the position=fill option would be the best one.

3a | Simple histogram

Now we have a new data set called iris and we need to understand the distribution of some of its continuous variables. A good place to start is a histogram, that represents the number of observations in different ranges as bars.

Note that histograms deal with continuous variables while bar plots with discrete, but are sometimes confused.

The function that we need is called geom_histogram and has the statistical transformation bin by default. In this case, bin divides the variable mapped to x in ranges and counts the number of values in each bin. The number of bins is controlled with the argument binwidth.

ggplot(data = iris, mapping = aes(x = Sepal.Width)) +
  geom_histogram(binwidth = 0.1)

3b | Multiple histograms

iris data contains information about three species of iris: setosa, versicolor and virginica. To see the distribution of the different species we can try to map the species to the filling colour. That’s easy with ggplot2!

ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) 

Stacked histograms are difficult to interpret and three separated subplots could actually work better. ggplot2 provides a simple way of creating small multiples or facets with the functions facet_grid and facet_wrap.

ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_grid(Species ~ .)

Exercise

Experiment with facet_grid and facet_wrap. For testing purposes, we can create an extra categorical variable by splitting Petal.Length in two groups.

iris$Petal.Type[iris$Petal.Length >= 4 ] <- "Long"
iris$Petal.Type[iris$Petal.Length < 4 ] <- "Short"

#Separate as before + separate according to length
ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_grid(Species ~ Petal.Type)

ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_wrap(Species ~ Petal.Type)

Which is the best subplot configuration to compare the distributions and why?

The best subplot configuration would be the wrap option as more space is occupied with useful data, the grid option creates 2 empty sublots, wasting space.

4a | Modify colours

So far we have used the default colour palettes for all our representations. We may need to change them to make them accessible to colourblind people, match the colour palette of our project or give meaningful values (e.g., red for positive and blue for negative). We can control the exact mapping of a variable to an aesthetic attribute with the functions scale_*.

ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_grid(Species ~ .) +
  scale_fill_manual(values = c("darkorange", "darkgray", "black"))

Note that scale functions update both the aesthetic mappings in the plot and in the legend.

4b | Change (or add) axis, legend and plot titles

We may also need to add a title to the plot or change the axis title. In ggplot2 axis and legend titles can be specified with name argument within a scale_* function. The title is set with ggtitle. You can also use the convenience function labs.

# We save the common part of the plot in a variable and then we can add more components with the "+" sign
p <- ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_grid(Species ~ .)

# Option A:
p + scale_fill_manual(values = c("darkorange", "darkgray", "black"), name = "Species name") +
  scale_x_continuous(name = "Sepal width") + 
  ggtitle("Iris sepal variation")

# Option B:
# p + scale_fill_manual(values = c("darkorange", "darkgray", "black")) +
#   labs(title = "Iris sepal variation", x = "Sepal width", fill = "Species name")

4c | Change theme

The appearence of ggplot2 plots is controlled by the themes. The default ggplot2 theme has a gray background and “is designed to put the data forward yet make comparisons easy”. You can change the general appearence by choosing a different theme with theme_* functions.

ggplot(data = iris, mapping = aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point() +
  theme_classic()

Exercise

Try other scale_fill_* functions in ggplot2 with pre-defined palettes, such as scale_fill_brewer and scale_fill_viridis_d. Which palette would you use to ensure that colourblind people can distinguish the colours?

The scale_fill_viridis palettes, as they are designed to be perceived by viewers with common forms of colour blindness

plot4<-ggplot(data = iris, mapping = aes(x = Sepal.Width, fill = Species)) +
  geom_histogram(binwidth = 0.1) +
  facet_grid(Species ~ .) +
  scale_fill_viridis_d()

plot4

Try subtitle, caption and tag arguments from the labs function. What are they for?

plot4+labs(title = "Iris sepal variation", x = "Sepal width", fill = "Species name", caption = "A plot with iris data", subtitle="3 species are represented in this plot", tag="Jan Izquierdo")

They are used to add extra information to plots

Which theme do you think that maximises the data-ink ratio?

ggplot(data = iris, mapping = aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point() +
  theme_minimal()

The theme option that maximizes data-ink ratio is theme_minimal in my opinion, as it has no background or gray colorings, it only displays the basic information to make the graph understandable.

Exercise

Save the plot p in a raster and a vector format with the same size. What differences do you observe?

Note: svg devices require svglite R package and other system libraries. Skip the exercise if you get an error!

ggsave(filename = "plot_ex5.png", plot = p, width = 6, height = 4)
#install.packages("svglite")
library(svglite)
ggsave(filename = "plot_ex5.svg", plot = p, width = 6, height = 4)

The difference that is more obvious is the file size, vector files occupy much less disk space than raster files

  Answer:

plot6<-ggplot(data = economics, mapping = aes(x = date, y = unemploy)) +
  geom_line()+
  labs(title = "Unemployment rates over time", x="Years", y="Unemployed individuals")+
  theme_classic() #Initially theme_minimal()

ggsave(filename = "plot6.svg", plot=plot6)